Authors

Abstract

The paper investigates the impact of infrastructure on economic growth of Pakistan using Jarque-Berra, White test and Breusch-Godfrey techniques for the period (1974-2011). Overall the result reveal that Gross Domestic Product, Gross Fixed Capital Formation, Per Capita Health Expenditures, Total generation of electricity (Hydel + Thermal + Nuclear),Total Road Lengths ,Total Telephone Lines and CPI play an important role in economic growth in Pakistan. More importantly the study finds that infrastructure development in Pakistan has significant positive contribution to growth. The experience from Pakistan suggests that it is necessary to design an economic policy that improves the infrastructure as well as gross fixed capital formation for sustainable economic growth in developing countries.

Keywords

Introduction

Infrastructure is basic physical and organizational structures needed for the operation of a society or enterprise or the services and facilities necessary for an economy to function. It can be generally defined as the set of interconnected structural elements that provide framework supporting an entire structure of development. It is an important term for judging a country or region’s development. Investment in infrastructure is part of the capital accumulation required for economic development and may have an impact on socioeconomic measures of welfare. The causality of infrastructure and economic growth has always been in debate. In developing nations, expansions in electric grids, roadways, and railways show marked growth in economic development. However, the relationship does not remain in advanced nations who witness more and more low rates of return on such infrastructure investments. Nevertheless, infrastructure yields indirect benefits through the supply chain, land values, small business growth, consumer sales, and social benefits of community development and access to opportunity. The most common classification of developing countries is based on economic indicators such as the Gross National Product per Capita. Income is indeed an important distinguishing criterion with the respect of development issue. Lowincome levels show a high correlation with among others, high population growth rates, high infant mortality rates, high total fertility and low life expectancy. However it should be noted that the average, says nothing at all about the distribution of income within a country. Transport infrastructure investment lead to changes in generalized transport costs, via shorter distances or high speeds, which give rise to reductions in fuel, capital, and labor costs. Such www.advancejournals.org Open Access Scientific Publisher Journal of Economic Research Journal of Economic Research 2 changes will have impacts in the transport system in the form of mode choice, choice of time of day and the generation and attraction of trips per zone. It is widely believed that the reduction in generalized transport costs lead to an increase in productivity in firms. However it is not always clear if the firms located in the investing country, province, district or other geographical area will benefit from the improved transport system. We investigate how telecommunications infrastructure affects economic growth. This issue is important and has received considerable attention in the popular press concerning the creation of the “information superhighway” and its potential impacts on the economy. Telecommunications infrastructure investment can lead to economic growth in several ways. Most obviously, investing in telecommunications infrastructure does itself lead to growth because its products cable, switches, etc. lead to increases in the demand for the goods and services used in their production. In addition, the economic returns to telecommunications infrastructure investment are much greater than the returns just on the telecommunication investment itself. Where the state of the telephone system is rudimentary, communications between firms is limited. The transaction cost of ordering, gathering information, searching for services are high. As the telephone system improves, the cost of doing business fall, and output will increase for individual firms in individual sector of the economy. “If the telephone does have an impact on nation’s economy, it will be through the improvement of the capabilities of managers to communicate with each other rapidly over increased distances” [Hardy (1980).

Objectives

The objective of this working paper is that

To check the impact of infrastructure on economic growth of Pakistan.

To check the Positive and significant impact of infrastructure on Economic growth of Pakistan.

Hypothesis

Infrastructure has positive effect on the economic growth of Pakistan.

Infrastructure contributes significantly and positively in economic growth of Pakistan.

Literature Review

Infrastructure development, both economic and social is one of the major determinants of the economic growth particularly in developing countries like Pakistan. Direct investment on infrastructure creates production facilities, stimulates economic activities, reduces the transaction & trade costs improvising competitiveness and provides employment opportunities to the poor. In much of the literature, Donaldson (2008) studies the effects of railroad construction in 19th century India using a difference-indifference approach. And Keller and Shue (2008) use a similar approach to look at the opening up of railways between regions of Germany. All these papers start from a trade framework where the effect of transportation infrastructure is studied from the point of view of market integration. The focus is on price convergence and changes in the relative price of factors along the lines predicted by trade models. Their results suggest that transportation infrastructure favors greater price convergence and that factor prices shift in the direction as predicted by trade theory. Sahoo, Natraj and Dash (2010) investigate the role of infrastructure in promoting the economic growth in China. Overall results reveal that infrastructure stock, labor force, public and private investments have played an important role in economic growth in China. More importantly they find that infrastructure development in China has significant positive contribution to growth than both positive and public investments. Further they check the unidirectional casualty from infrastructure to economic growth that justifying the high spending by China on infrastructure development. Fontenla and Noriega (2005) studied the impact of public infrastructure on output level in Mexico and also check the optimality with which the level of infrastructure have been set. They are basically concerned to look at the long-run effect of shock to infrastructure to real output. Their results suggests that long-run derivatives of kilowatts for electricity, roads and phone lines, and finds that shocks to infrastructure have positive and significant effects on real output for all three measure of infrastructure. For electricity and roads, the effect become significant after 7 and 8 years, respectively, whereas for phones, the effects on growth in significant only after 13 years. These effects on infrastructure on output are in agreement with growth models where longrun growth is driven by endogenous factors of production. However, their results indicate that none of these variables seem to be set at growth maximizing levels. Esfahani and Ramirez (1999) made cross-country analysis by using identifiable recursive system, and estimates the structural model of infrastructure and economic growth and the model indicate that the contribution of infrastructure to GDP is substantial and in general exceeds cost of provision of those services. Schiffbauer (2007) analyzes the impact of infrastructure capital on different sources of economic growth. The literature on infrastructure and economic growth mainly focuses on the private and public capital investments, but here they also demonstrate the link between (telecommunication) infrastructure capital and endogenous technological change in the context of the dynamic panel estimation applying the aggregate country as well as US firm level data. By using the different dynamic panel techniques they examine the coherence between infrastructure variables and different sources of economic growth. The main empirical finding is that the increase in telecommunication infrastructure during the last 30 years enhanced R&D investments but did not affect the accumulation of physical and human capital in our sample. R&D growth model also emphasizes on costreducing features of infrastructure capital and demonstrate the potential link between the levels of infrastructure capital and endogenous technological change. Boopen (2006) Studied about the Empirical evidences on the importance of transport capital development in fastening productivity and economic development for panel sets, particularly for African countries and island state cases, have been very scare in the literature. This study analysis the transport capital to growth for two different sets of data namely for sub Saharan African countries and also for a developing states (SIDS) using both cross-sectional and Journal of Economic Research Journal of Economic Research 3 panel data analysis. By using simple OLS techniques and auto regressive technique and GMM methods, they concluded that transport capital has been a contributor to economic progress of these countries. So this analysis further reveals that in SSA case, the productivity of transport capital stock is superior as compared to that of overall capital. But in case of SIDS where the transport capital seen to have the average productivity level of overall capital stock.

Data and Source

The variables used for empirical analysis in this study are as follows

Dependent Variables

Gross Domestic Product (GDP)

Independent Variables

Gross Fixed Capital Formation (GFCF)

Per Capita Health Expenditures (PCHE)

Total generation of electricity (TGOE) (Hydral + Thermal + Nuclear)

Total Road Lengths (TRL)

Total Telephone Lines (TTL)

CPI

Data sources of these variables are World Development Indicators (WDI) and State Bank of Pakistan (SBP). Sample period includes 35 years from 1974 to 2011. Per Capita Health Expenditures is converted in dollars ($) by dividing it with the average quarterly exchange rate of 2000. Quarterly exchange rate takes into account the fluctuations and averaging dampens the effect of these fluctuations thus making this series more reasonable. One missing value of Total Generation of electricity was generated through forward extrapolation.

Methodology

All the variables in the model are used in log forms as log form shows relative growth and also to run a double-log model and check for the elasticity of GDP with respect to all independent variables. An additional benefit of doublelog model is that it makes interpretation of results more objective and meaningful. GFCF, TGOE and PCHE are used as proxies for infrastructure. Through our empirical analysis, we are going to check the impact of infrastructure on the economic growth of Pakistan. But almost all the economic variables are non-stationary at their level form. So we check for the stationary of the variables through correlograms and more rigorous augmented dickey fuller test and Philips Peron test at level form. Results suggest that all the variables follow unit root process. So we go for appropriate transformation. Iterative mining suggest that TGOE and GFCF are I(1) while GDP and PCHE are I(2) at level form. So neither Co integration is applicable because the order of integration is not same nor the ARDL as dependent variable is I(2). Thus we used Ordinary least Squares method in the framework of multiple regression analysis to approach a deterministic relation. This exercise proved to be useful as data fits the model reasonably well.

Estimation Results

All the coefficients are statistically significant even at 1% level of significance and their signs are according to priori expectations. Adjusted R2 is 0.80 showing the high explanatory power of the model and Durbin Watson statistic is very close to 2 nullifying the existence autocorrelation in the residual terms. Additional tests are also applied to check for various dimensions of model reliability and adequacy. Jarque-Berra test for the normality confirms error terms to be normally distributed. Breusch-Godfrey serial correlation LM test confirms no serial correlation and White test indicate homoskedasticity. Stability of coefficients is checked through Remsy RESET and confidence ellipse test. More formal Wald test for the collective significance of coefficients is applied. Results suggest that 1% increase in Gross Fixed Capital Formation causes GDP to rise by 0.44%. While 1 unit proportionate increase in Per Capita Health Expenditure and Total Generation of electricity causes GDP to surge upward by 0.27% and 0.043% respectively.

(Descriptive Statistics and Jarque-Bera Test)

Test 2 resu1lt: (Granger Causality tests)

Pair wise Granger Causality Tests

Sample: 1974 2011

Lags: 2

Null Hypothesis:

Obs

F-Statistic

Prob.

LGFCF does not Granger Cause LGDP

35

0.02109

0.9791

LGDP does not Granger Cause LGFCF

4.35940

0.0218

LPCHE does not Granger Cause LGDP

35

1.29369

0.2891

LGDP does not Granger Cause LPCHE

11.3217

0.0002

LTGOE does not Granger Cause LGDP

35

1.75099

0.1909

LGDP does not Granger Cause LTGOE

3.05351

0.0621

LPCHE does not Granger Cause LGFCF

35

3.69733

0.0367

LGFCF does not Granger Cause LPCHE

2.00461

0.1524

LTGOE does not Granger Cause LGFCF

35

4.59051

0.0182

LGFCF does not Granger Cause LTGOE

4.20019

0.0246

LTGOE does not Granger Cause LPCHE

35

0.69961

0.5047

LPCHE does not Granger Cause LTGOE

4.08204

0.0270

Test 3 result: (OLS)

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

14.07944

2.330674

6.040930

0.0000

LGFCF

0.437514

0.100309

4.361644

0.0001

LPCHE

0.268831

0.043500

6.179984

0.0000

LTGOE

0.043450

0.015486

2.805762

0.0087

AR(1)

0.611012

0.135973

4.493619

0.0001

MA(1)

0.507549

0.177257

2.863358

0.0076

R-squared

0.806611

Mean dependent var

24.58235

Adjusted-R

0.806046

S.D. dependent var

0.534708

squared

S.E. of regression

0.033623

Akaike info criterion

-3.796225

Sum squared resid

0.033914

Schwarz criterion

-3.532306

Log likelihood

74.33206

Hannan-Quinn criteria

-3.704110

F-statistic

1764.394

Durbin-Watson stat

2.022002

Prob(F-statistic)

0.000000

Inverted AR Roots

.61

Inverted MA Roots

-.51

Test 4: (Breusch-Godfrey Serial Correlation LM Test)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

0.668333

Prob. F(2,28)

0.5205

Obs*R-squared

1.638846

Prob. Chi-Square(2)

0.4407

Presample missing value lagged residuals set to zero

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

0.011603

2.380381

0.004874

0.9961

LGFCF

-6.46E-05

0.102435

-0.000631

0.9995

LPCHE

-0.006368

0.044490

-0.143138

0.8872

LTGOE

-0.000581

0.015668

-0.037058

0.9707

AR(1)

-0.121835

0.215044

-0.566557

0.5755

MA(1)

0.754894

0.684987

1.102057

0.2798

RESID(-1)

-0.631933

0.632767

0.998682

0.3265

RESID(-2)

0.534704

0.481941

1.109478

0.2767

R-squared

0.045523

Mean dependent var

-0.000197

Adjusted R-squared

-0.193096

S.D. dependent var

0.031128

S.E. of regression

0.034001

Akaike info criterion

-3.731748

Sum squared resid

0.032369

Schwarz criterion

-3.379855

Log likelihood

75.17146

Hannan-Quinn criteria

-3.608928

F-statistic

0.190779

Durbin-Watson stat

1.991681

Prob(F-statistic)

0.985022

Test 5: (Heteroskedasticity Test: White)

F-statistic

1.386810

Prob. F(27,8)

0.3285

Obs*R-squared

29.66251

Prob. Chi-Square(27)

0.3295

Scaled explained SS

12.97645

Prob. Chi-Square(27)

0.9894

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

5.945761

2.679558

2.218933

0.0573

GRADF_01

-228.2123

108.0098

-2.112885

0.0676

GRADF_01^2

832.2272

425.4178

1.956259

0.0862

GRADF_01*GRADF_02

-11.21155

22.83678

-0.490943

0.6367

GRADF_01*GRADF_03

12.11357

8.312635

1.457248

0.1832

GRADF_01*GRADF_04

-48.87087

27.87784

-1.753037

0.1177

GRADF_01*GRADF_05

-33.49590

14.73902

-2.272600

0.0527

GRADF_01*GRADF_06

-95.86960

42.00134

-2.282537

0.0519

GRADF_02

2.151583

5.336538

0.403179

0.6974

GRADF_02^2

0.055711

0.167395

0.332811

0.7478

GRADF_02*GRADF_03

0.015794

0.117103

0.134872

0.8960

GRADF_02*GRADF_04

0.020115

0.031491

0.638759

0.5408

GRADF_02*GRADF_05

0.251368

0.381492

0.658907

0.5285

GRADF_02*GRADF_06

-0.017153

0.218251

-0.078592

0.9393

GRADF_03

-3.240265

2.234642

-1.450015

0.1851

GRADF_03^2

-0.016775

0.027662

-0.606428

0.5610

GRADF_03*GRADF_04

0.009721

0.013292

0.731339

0.4854

GRADF_03*GRADF_05

0.050262

0.111211

0.451954

0.6633

GRADF_03*GRADF_06

-0.058481

0.113252

-0.516375

0.6196

GRADF_04

12.47277

7.090086

1.759184

0.1166

GRADF_04^2

0.002532

0.002849

0.888807

0.4000

GRADF_04*GRADF_05

-0.052651

0.124083

-0.424323

0.6825

GRADF_04*GRADF_06

0.023992

0.134657

0.178170

0.8630

GRADF_05

7.276765

3.772467

1.928914

0.0899

GRADF_05^2

-0.832576

0.461648

-1.803485

0.1090

GRADF_05*GRADF_06

1.316010

0.528004

2.492423

0.0374

GRADF_06

24.79094

10.34056

2.397447

0.0433

GRADF_06^2

-0.510119

0.279641

-1.824190

0.1056

R-squared

0.806611

Mean dependent var

0.000942

Adjusted R-squared

0.229819

S.D. dependent var

0.001072

S.E. of regression

0.000941

Akaike info criterion

-11.04743

Sum squared resid

7.09E-06

Schwarz criterion

-9.815807

Log likelihood

226.8538

Hannan-Quinn criter

-10.61756

F-statistic

1.386810

Durbin-Watson stat

2.451071

Prob(F-statistic)

0.328531

Test 6: (Ramsey RESET Test)

F-statistic

5.586705

Prob. F(2,28)

0.0091

Log likelihood ratio

12.08857

Prob. Chi-Square(2)

0.0024

MA Backcast: 1972

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

7.536593

6.297793

1.196704

0.2415

LGFCF

-0.153069

0.114859

-1.332660

0.1934

LPCHE

0.013077

0.106389

0.122918

0.9031

LTGOE

0.027589

0.031279

0.882046

0.3853

FITTED^2

0.056712

0.031649

1.791904

0.0840

FITTED^3

-0.000944

0.000706

-1.338052

0.1916

AR(1)

0.424934

0.402859

1.054795

0.3005

MA(1)

-0.997456

0.134862

-7.396149

0.0000

R-squared

0.806611

Mean dependent var

24.58235

Adjusted R-squared

0.806046

S.D. dependent var

0.534708

S.E. of regression

0.029424

Akaike info criterion

-4.020908

Sum squared resid

0.024241

Schwarz criterion

-3.669015

Log likelihood

80.37634

Hannan-Quinn criter.

-3.898088

F-statistic

1647.246

Durbin-Watson stat

1.831350

Prob(F-statistic)

0.000000

Inverted AR Roots

.42

Inverted MA Roots

1.00

Test 7

Test 8: (Wald Test)

Test Statistic

Value

df

Probability

F-statistic

732.7938

(5, 30)

0.0000

Chi-square

3663.969

5

0.0000

Null Hypothesis Summary:

Normalized Restriction (= 0)

Value

Std. Err.

C(1) – C(6)

13.57189

2.329195

C(2) – C(6)

-0.070035

0.206707

C(3) – C(6)

-0.238718

0.181060

C(4) – C(6)

-0.464100

0.180140

C(5) – C(6)

0.103462

0.266811

Restrictions are linear in coefficients.

Granger causality tests indicate that there is one way causality between GDP, PCHE, TGOE and GFCF. Granger causality tests are also utilized to separate the short run effects from long run effects. In short run, GDP induces GFCF, TGOE and PCHE to grow. This makes our conclusion more robust as it was expected that increase in GDP provoke GFCF, PCHE and TGOE in short run while in the long run all these factors contribute to the economic growth. Our results confirm that infrastructure plays a significant role in economic growth of Pakistan.

Conclusion & Recommendation

This study shows that Infrastructure have positive and significant impact on economics growth of Pakistan. On the basis of our empirical analysis, it is strongly recommended that Government must take aggressive moves to expand the infrastructure facilities and improve the quality of available infrastructure to fulfill the requirement of economic growth at a faster pace. As mentioned in new growth strategy by planning and development commission of Pakistan that there is a need for an effort to fully utilize the available infrastructure for economic growth and our results are in conformity with it as Total Generation of Electricity is positively associated with GDP growth. Generating capacity of Pakistan is less than the installed generating capacity of electricity and there is a need to bridge this gap.